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Brown Bag Lunch Seminar Series on Remote Sensing
Combining remote sensing and deep learning to understand ecosystem phenology
Date: 30 November 2023 (Thursday)
Time: 11:30-12:30 (HKT)
Mode: In Person (Limited seats, registration is required.)
Venue: CLL, 10/F, The Jockey Club Tower, Centennial Campus, HKU
Abstract:
Phenology is a crucial indicator for ecosystems, providing information for plant monitoring. For example, leaf phenology controls the seasonality of carbon and water fluxes, while flower phenology controls the reproductive success of angiosperms. At the same time, ecosystem phenology is sensitive to changes in climate, leading to potential cascading changes in ecosystems as the effects anthropogenic climate change continue to worsen. Across two studies, we developed deep learning methods to improve ways to monitor leaf and flower phenology within tropical forests with high species richness using phenocams and drone imagery, respectively. These models offer ways of rapidly extracting information on ecosystem phenology, providing important information on ecosystem response to climate change.
Dr. Calvin Lee
Post-doctoral Research Fellow, School of Biological Sciences, HKU
Calvin Lee is a postdoctoral researcher within the School of Biological Sciences at HKU. His research focuses on integrating different analysis methods to use remote sensing data for conservation. He has published papers on methods of assessing ecosystem risk and status, including changes in area and degradation. More recently, his work involved using deep learning methods to quantify leaf and flower phenology within tropical forests using phenocam and drone data. He is currently working on quantifying the ecosystem services provided by Hong Kong’s forests, and the potential impacts of successful forest restoration within the city.
